Omer Gottesman

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 11 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 43 citations into it; #32,921 of 35,345 by trust here, #14,181 by citations
• Reinforcement Learning — 39 citations into it; #1,625 of 2,242 by trust here, #992 by citations
• Statistics and Probability — 20 citations into it; #12,087 of 12,932 by trust here, #6,310 by citations
• Epidemiology — 16 citations into it
• Management Science and Operations Research — 15 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Software — 7 citations into it

Whom Omer Gottesman trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Philip S. Thomas
2. Nan Jiang
3. Richard S. Sutton
4. Doina Precup
5. Lihong Li
6. Damien Ernst
7. Emma Brunskill
8. Satinder Singh
9. Matthieu Komorowski
10. Andrew Barto

Who trusts Omer Gottesman overall

1. Nathan Kallus
2. Chao Yu
3. Joseph D. Futoma
4. Ziyang Tang
5. Masatoshi Uehara
6. Aniruddh Raghu
7. Bernhard Schölkopf
8. Khaled Ben Letaief
9. Scott Fujimoto
10. Chaochao Lu

Papers from Omer Gottesman's perspective

Ordered by how strongly the researchers Omer Gottesman trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Reinforcement Learning: An Introduction
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Evaluating Reinforcement Learning Algorithms in Observational Health Settings (2018) — 151 citations
• Representation Balancing MDPs for Off-Policy Policy Evaluation (2018) — 75 citations
• Guidelines for reinforcement learning in healthcare (2019) — 449 citations
• Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions (2020) — 69 citations
• Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters (2018) — 37 citations
• Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning (2018) — 110 citations